PREDICTIVE ML · SPEQ SYNTHESIS
Document Intelligence & Extraction
An AI system that reads, extracts, and classifies content from documents and forms, automating tasks such as batch-record review, complaint intake and triage, and structured data entry from unstructured sources.
Because it transfers information into regulated records and routes regulated events, an extraction or classification error becomes a data-integrity issue at the point where the AI output is trusted as the transcribed value.
What a system class is not
An AI system class describes a shape of system, not a product and not an approval pathway. SPEQ does not qualify, validate, or endorse any implementation, and no regulator recognises these classes as a category.
Weighted score 20/32 (decision consequence and model influence weighted most heavily) places this in the high tier.
Computed deterministically from four Context-of-Use factors — decision consequence, model influence, data sensitivity, and change dynamics. A transparent scoping aid, not a validated risk-assessment system.
SCOPE THE ASSURANCE STRATEGY → CSA WORKBENCHUsed to convert unstructured documents into structured, actionable data across manufacturing and quality, influencing what value lands in a record and how a complaint or record is categorized and routed.
Extracted values and classifications feed regulated records and event routing, so a misread field or a wrong category can corrupt a batch record or misdirect a complaint that required prompt handling.
human in the loop
Because extracted data and classifications enter regulated records, a person must verify low-confidence or consequential outputs against the source, and accountability for the record value stays human.
- Extraction errors on handwriting, poor scans, or unusual layouts can transcribe an incorrect value into a regulated record where it is then trusted as accurate.
- Misclassification can route a document or complaint down the wrong workflow, delaying or downgrading an event that required prompt and specific regulated handling.
- The model can degrade on document formats or vocabulary it was not trained on, so new templates or edge-case documents quietly increase the error rate.
- Confidence scores can be poorly calibrated, so low-quality extractions are accepted automatically while genuinely correct ones are needlessly escalated.
- Automation bias leads reviewers to accept extracted fields without checking against the source document, collapsing the verification the data-integrity model depends on.
- Qualify extraction and classification accuracy per document type and field, reporting error rates against a representative labeled set rather than a single aggregate score.
- Calibrate and use confidence thresholds so uncertain extractions are routed to human verification instead of being silently accepted into a regulated record.
- Preserve source-to-record traceability so any extracted value can be checked against the original document, maintaining data integrity and enabling investigation.
- Monitor performance as document formats, templates, and vocabulary evolve, and re-qualify when new document types enter the workflow at scale.
- Provide reviewers with the source alongside the extracted output to counter automation bias and keep verification against the original genuinely in the loop.
Standards SPEQ maps to this class
AI-governance frameworks
The AI-specific shelf that defines “quality AI” — see Good AI Practice.
SPEQ synthesis — applied AI-assurance judgment to help you scope your own Context-of-Use assessment and validation. Not regulatory guidance, not an AI classification service, and not a substitute for your documented risk assessment.